Browsing by Author "Olcer, Didem"

Now showing 1 - 4 of 4

Detection of Visual Impairment From Retinal Fundus Images with Deep Learning
(2022) Olcer, Didem; Erdas, Cagatay Berke
A Machine Learning Based Approach to Detect Survival of Heart Failure Patients
(2020) Erdas, Cagatay Berke; Olcer, Didem
One of the diseases with high prevalence among the consequences of cardiovascular diseases is heart failure. Heart failure is a condition in which the muscles in the heart wall become faded and dilated, limiting the heart's ability to pump blood. As time passes, the heart cannot meet the proper blood requirement in the body, and as a result, the person has difficulty breathing. As the human age increases, the incidence of heart failure gradually increases, and the rate of mortality due to heart failure also increases. In this context, close monitoring of people suffering from this disease will significantly increase the survival rate. In this study, a machine learning-based system is proposed to predict the mortality-survival status of patients with heart failure. Thus, by identifying people with mortality risk, the survival probability of the patients may increase with more effective and close follow-up.
Prediction of Frailty Grade Using Machine Learning Models
(2022) Erdas, Cagatay Berke; Olcer, Didem
Nowadays, frailty is becoming a major issue for the aging population. Frailty grading is important for patient quality of life because it is a geriatric syndrome of decreased physiological reserve that leads to increased susceptibility to physical stress factors and susceptibility to cardiovascular diseases. In this context, this study seeks a solution to the fragility rating regression problem with K Nearest Neighbor, Decision Tree, Random Forest, Extra Trees and CatBoost models using time domain features extracted from the triaxial accelerometer signals collected during the TUG test. Moreover, estimating the grade of frailty involved in addition to diagnosing people with the disease will provide physicians with more detailed information about the patient and allow accurate and effective treatment/supportive treatment.
Quality assessment of web-based information on type 2 diabetes
(2021) Olcer, Didem; 0000-0001-7736-1021
Purpose This paper proposes a framework that automatically assesses content coverage and information quality of health websites for end-users. Design/methodology/approach The study investigates the impact of textual and content-based features in predicting the quality of health-related texts. Content-based features were acquired using an evidence-based practice guideline in diabetes. A set of textual features inspired by professional health literacy guidelines and the features commonly used for assessing information quality in other domains were also used. In this study, 60 websites about type 2 diabetes were methodically selected for inclusion. Two general practitioners used DISCERN to assess each website in terms of its content coverage and quality. Findings The proposed framework outputs were compared with the experts' evaluation scores. The best accuracy was obtained as 88 and 92% with textual features and content-based features for coverage assessment respectively. When both types of features were used, the proposed framework achieved 90% accuracy. For information quality assessment, the content-based features resulted in a higher accuracy of 92% against 88% obtained using the textual features. Research limitations/implications The experiments were conducted for websites about type 2 diabetes. As the whole process is costly and requires extensive expert human labelling, the study was carried out in a single domain. However, the methodology is generalizable to other health domains for which evidence-based practice guidelines are available. Practical implications Finding high-quality online health information is becoming increasingly difficult due to the high volume of information generated by non-experts in the area. The search engines fail to rank objective health websites higher within the search results. The proposed framework can aid search engine and information platform developers to implement better retrieval techniques, in turn, facilitating end-users' access to high-quality health information. Social implications Erroneous, biased or partial health information is a serious problem for end-users who need access to objective information on their health problems. Such information may cause patients to stop their treatments provided by professionals. It might also have adverse financial implications by causing unnecessary expenditures on ineffective treatments. The ability to access high-quality health information has a positive effect on the health of both individuals and the whole society. Originality/value The paper demonstrates that automatic assessment of health websites is a domain-specific problem, which cannot be addressed with the general information quality assessment methodologies in the literature. Content coverage of health websites has also been studied in the health domain for the first time in the literature.